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Learning to reconstruct quirky tracks

Year: 2026
Type: article
Abstract: Analysis of data from particle physics experiments traditionally sacrifices some sensitivity to new particles for the sake of practical computability, effectively ignoring some potentially striking signatures. However, recent advances in machine learning (ML)–based tracking allow for new inroads into previously inaccessible territory, such as reconstruction of tracks that do not follow helical trajectories. This paper presents a demonstration of the capacity of ML-based tracking to reconstruct the oscillating trajectories of quirks, particles charged under an unbroken QCD-like gauge symmetry. The technique used is not specific to quirks, and opens the door to a program of searching for many kinds of nonstandard tracks.
DOI: 10.1103/dvzc-lgj1
Authors: Qiyu Sha, Daniel Murnane, Max Fieg, Shelley Tong, Mark Zakharyan, Yaquan Fang, Daniel Whiteson
Language: English
Open Access status: hybrid
Funders: Danish Data Science Academy, National Science Foundation, U.S. Department of Energy, Chinese Academy of Sciences, Novo Nordisk, Novo Nordisk Fonden, Office of Science, Institute of High Energy Physics, High Energy Physics
Awards: G1178403614, Theoretical Particle Physics and Cosmology at UC Irvine, G3154201110, G402501366, Graduate Research Fellowship (GRFP), Collaborative Research: FASER and FASERnu at the Large Hadron Collider, G8825220333
OpenAlex ID: W4403818897
FWCI:
Cites: 55
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Related to: 10